Promising strategies for cervical cancer screening in the post-human papillomavirus vaccination era
Bibliographic record
Abstract
Human papillomavirus (HPV) vaccination is expected to reduce the burden of cervical cancer in most settings; however, it is also expected to interfere with the effectiveness of screening. In the future, maintaining Pap cytology as the primary cervical screening test may become too costly. As the prevalence of cervical dysplasias decreases, the positive predictive value of the Pap test will also decrease, and, as a result, more women will be referred for unnecessary diagnostic procedures and follow-up. HPV DNA testing has recently emerged as the most likely candidate to replace cytology for primary screening. It is less prone to human error and much more sensitive than the Pap smear in detecting high-grade cervical lesions. Incorporating this test would improve the overall quality of screening programs and allow spacing out screening tests, while maintaining safety and lowering costs. Although HPV testing is less specific than Pap cytology, this issue could be resolved by reserving the latter for the more labour-efficient task of triaging HPV-positive cases. Because most HPV-positive smears would contain relevant abnormalities, Pap cytology would be expected to perform with sufficient accuracy under these circumstances. HPV Pap triage would also provide a low-cost strategy to monitor long-term vaccine efficacy. Although demonstration projects could start implementing HPV testing as a population screening tool, more research is needed to determine the optimal age to initiate screening, the role of HPV typing and other markers of disease progression, and appropriate follow-up algorithms for HPV-positive and Pap-negative women.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".